Report: 5 Key Trends for the Future of AI


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Artificial intelligence (AI) is a fundamental catalyst for digital business, according to a new report from Gartner† The past 10 years represent a breakthrough period in AI development, fueled by the convergence of factors including the emergence of big data, advancements in computing infrastructure, new machine learning techniques, the emergence of cloud computing and the vibrant open-source ecosystem. .

When evaluating the trends that will shape the future of AI in the enterprise, there are five trends that Gartner sees as the most important. Technology leaders must leverage these emerging AI trends to democratize and advance its responsible use, adapt compound techniques, leverage AI for real-time analytics at the edge, and harness its generative powers.

Democratized AI will make AI accessible to a wide range of users. From discovering unexpected opportunities by identifying trends hidden in large data sets, to augmenting and assisting employees in expanding their knowledge, democratized AI will touch every aspect of our lives.

Composite AI is the ability to deploy a variety of AI techniques tailored to appropriate use cases. It brings the power of AI to a wider range of use cases and users, and is gaining popularity as it improves AI’s versatility, efficiency, and adaptability.

Edge AI enables digital moments by leveraging AI for real-time analytics closer to data sources. Gartner predicts that by 2025, more than 50% of all data analysis by deep neural networks will be at the edge, down from less than 10% by 2021.

Responsible AI is an umbrella term for making appropriate business and ethical choices when adopting AI. It requires consideration of business and social value, risk, trust, transparency, fairness, bias, explainability, accountability, security, privacy and regulatory compliance. Responsible AI is becoming increasingly important amid growing regulatory oversight, consumer expectations and emerging sustainability goals.

Generative AI is the use of AI to generate new artifacts and create groundbreaking products. Going out, generative AI efforts have focused on creating media content, such as photo-realistic depictions of people and things, but it can also be used to generate codes, create synthetic tabular data, and design drugs and materials with specific properties.

Read the full report by Gartner.

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